Portrait of Yoshua Bengio

Yoshua Bengio

Core Academic Member
Canada CIFAR AI Chair
Full Professor, Université de Montréal, Department of Computer Science and Operations Research Department
Scientific Director, Leadership Team
Observer, Board of Directors, Mila
Research Topics
Causality
Computational Neuroscience
Deep Learning
Generative Models
Graph Neural Networks
Machine Learning Theory
Medical Machine Learning
Molecular Modeling
Natural Language Processing
Probabilistic Models
Reasoning
Recurrent Neural Networks
Reinforcement Learning
Representation Learning

Biography

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For more information please contact Julie Mongeau, executive assistant at julie.mongeau@mila.quebec.

Yoshua Bengio is recognized worldwide as a leading expert in AI. He is most known for his pioneering work in deep learning, which earned him the 2018 A.M. Turing Award, “the Nobel Prize of computing,” with Geoffrey Hinton and Yann LeCun.

Bengio is a full professor at Université de Montréal, and the founder and scientific director of Mila – Quebec Artificial Intelligence Institute. He is also a senior fellow at CIFAR and co-directs its Learning in Machines & Brains program, serves as scientific director of IVADO, and holds a Canada CIFAR AI Chair.

In 2019, Bengio was awarded the prestigious Killam Prize and in 2022, he was the most cited computer scientist in the world by h-index. He is a Fellow of the Royal Society of London, Fellow of the Royal Society of Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada. In 2023, he was appointed to the UN’s Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology.

Concerned about the social impact of AI, Bengio helped draft the Montréal Declaration for the Responsible Development of Artificial Intelligence and continues to raise awareness about the importance of mitigating the potentially catastrophic risks associated with future AI systems.

Current Students

Research Intern - McGill University
Research Intern - Université de Montréal
PhD - Université de Montréal
Collaborating Alumni
Research Intern - Université du Québec à Rimouski
Independent visiting researcher
Co-supervisor :
PhD - Université de Montréal
Research Intern - UQAR
PhD - Université de Montréal
Independent visiting researcher - MIT
Collaborating researcher - N/A
Principal supervisor :
PhD - Université de Montréal
Postdoctorate - Université de Montréal
Co-supervisor :
Collaborating researcher - KAIST
Collaborating Alumni - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
Collaborating researcher - Université Paris-Saclay
Principal supervisor :
PhD - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
PhD - Massachusetts Institute of Technology
PhD - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Research Intern - Barcelona University
Research Intern - Université de Montréal
Collaborating researcher - Université de Montréal
Research Intern
Postdoctorate - Université de Montréal
Co-supervisor :
Independent visiting researcher - Technical University Munich (TUM)
PhD - Université de Montréal
Research Intern - Université de Montréal
Master's Research - Université de Montréal
Co-supervisor :
Research Intern - Université de Montréal
Collaborating researcher - Université de Montréal
PhD - Université de Montréal
Collaborating Alumni - Université de Montréal
PhD - Université de Montréal
Collaborating Alumni - Université de Montréal
Collaborating Alumni
Collaborating Alumni
PhD - Université de Montréal
Principal supervisor :
Research Intern - Imperial College London
PhD - Université de Montréal
Research Intern - Université de Montréal
Collaborating Alumni - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
Postdoctorate - Université de Montréal
Collaborating Alumni
Collaborating researcher - Université de Montréal
PhD - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
Principal supervisor :
Postdoctorate - Université de Montréal
Principal supervisor :
Independent visiting researcher - Université de Montréal
Independent visiting researcher - Hong Kong University of Science and Technology (HKUST)
Collaborating researcher - Ying Wu Coll of Computing
PhD - University of Waterloo
Principal supervisor :
PhD - Max-Planck-Institute for Intelligent Systems
PhD - Université de Montréal
Postdoctorate - Université de Montréal
Independent visiting researcher - Université de Montréal
Postdoctorate - Université de Montréal
Independent visiting researcher - Université de Montréal
PhD - Université de Montréal
Principal supervisor :
Research Intern - Université de Montréal
PhD - Université de Montréal
Postdoctorate - Université de Montréal
Master's Research - Université de Montréal
Research Intern - Université de Montréal
Research Intern - Université de Montréal
Master's Research - Université de Montréal
Collaborating Alumni
Independent visiting researcher - Technical University of Munich
PhD - École Polytechnique Montréal Fédérale de Lausanne
Postdoctorate - Polytechnique Montréal
Co-supervisor :
PhD - Université de Montréal
Co-supervisor :
Postdoctorate - Université de Montréal
Collaborating researcher - Valence
Principal supervisor :
Postdoctorate - Université de Montréal
Co-supervisor :
Collaborating researcher - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)
Principal supervisor :
PhD - Université de Montréal
Collaborating Alumni - Université de Montréal
Collaborating researcher - KAIST
Research Intern - Université de Montréal
PhD - McGill University
Principal supervisor :
PhD - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
PhD - McGill University
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Publications

Theano: A CPU and GPU Math Compiler in Python
James Bergstra
Olivier Breuleux
Frédéric Bastien
Pascal Lamblin
Razvan Pascanu
Guillaume Desjardins
Joseph P. Turian
David Warde-Farley
Theano is a compiler for mathematical expressions in Python that combines the convenience of NumPy's syntax with the speed of optimized nati… (see more)ve machine language. The user composes mathematical expressions in a high-level description that mimics NumPy's syntax and semantics, while being statically typed and functional (as opposed to imperative). These expressions allow Theano to provide symbolic differentiation. Before performing computation, Theano optimizes the choice of expressions, translates them into C++ (or CUDA for GPU), compiles them into dynamically loaded Python modules, all automatically. Common machine learn- ing algorithms implemented with Theano are from 1:6 to 7:5 faster than competitive alternatives (including those implemented with C/C++, NumPy/SciPy and MATLAB) when compiled for the CPU and between 6:5 and 44 faster when compiled for the GPU. This paper illustrates how to use Theano, outlines the scope of the compiler, provides benchmarks on both CPU and GPU processors, and explains its overall design.
L AUGHING H YENA D ISTILLERY Extracting Compact Recurrences From Convolutions
∗. StefanoMassaroli
∗. MichaelPoli
∗. DanielY.Fu
Hermann Kumbong
Rom N. Parnichkun
Aman Timalsina
David W. Romero
Quinn McIntyre
Beidi Chen
Atri Rudra
Ce Zhang
Christopher Re
Stefano Ermon
Recent advances in attention-free sequence models rely on convolutions as alternatives to the attention operator at the core of Transformers… (see more). In particular, long convolution sequence models have achieved state-of-the-art performance in many domains, but incur a significant cost during auto-regressive inference workloads – naively requiring a full pass (or caching of activations) over the input sequence for each generated token – similarly to attention-based models. In this paper, we seek to enable O (1) compute and memory cost per token in any pre-trained long convolution architecture to reduce memory footprint and increase throughput during generation. Concretely, our methods consist in extracting low-dimensional linear state-space models from each convolution layer, building upon rational interpolation and model-order reduction techniques. We further introduce architectural improvements to convolution-based layers such as Hyena : by weight-tying the filters across channels into heads , we achieve higher pre-training quality and reduce the number of filters to be distilled. The resulting model achieves 10 × higher throughput than Transformers and 1 . 5 × higher than Hyena at 1 . 3 B parameters, without any loss in quality after distillation.